1. Field
Certain aspects of the present disclosure generally relate to neural system engineering and, more particularly, to systems and methods detecting and recovering from errors in a neural network.
2. Background
An artificial neural network, which may comprise an interconnected group of artificial neurons (i.e., neuron models), is a computational device or represents a method to be performed by a computational device. Artificial neural networks may have corresponding structure and/or function in biological neural networks. In some cases, artificial neural networks may provide innovative and useful computational techniques for certain applications in which traditional computational techniques are cumbersome, impractical, or inadequate. Because artificial neural networks can infer a function from observations, such networks are particularly useful in applications where the complexity of the task or data makes the design of the function by conventional techniques burdensome. Still, neural networks may fail or experience unexpected behavior. Thus, it is desirable to provide a neuromorphic receiver to detect and recover from errors in a neural network.